Manual loss weights adaptation in TF2.0 - Githubissues

lululxvi / deepxde

A library for scientific machine learning and physics-informed learning

https://deepxde.readthedocs.io

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Manual loss weights adaptation in TF2.0 #1656

Open haison19952013 opened 9 months ago

haison19952013 commented 9 months ago

What?

Supported and tested backend: tensorflow
Details:
- Provide a way to change the loss weights dynamically via callbacks without the need to recompile the model
- Currently works for non-gradient-based adaptive loss weight scheme

Why?

Motivation
Help deepxde users can formulate their non-gradient-based adaptive loss weights scheme

How?

In model.py:
- Add loss_weights as instances for functions that work for tensorflow backend
In callbacks.py, give some examples on how to define a calback to change the loss_weights
- Add ManualDynamicLossWeight: to change the loss weights based on the specified index
- Add PrintLossWeight: to display the loss weights based on the specified period

Testing?

A working example is given in deepxde\examples\pinn_inverse\elliptic_inverse_field_manual_dynamic_loss_weights.py

Future work

Work on gradient-based adaptive loss weight scheme

lululxvi commented 9 months ago

Format the code via black https://github.com/psf/black

haison19952013 commented 8 months ago

Format the code via black https://github.com/psf/black

Updated

pescap commented 5 months ago

@haison19952013, do you plan to keep working on this PR? If not, I will continue the work.